The Art of Designing an Enterprise-Level Data Architecture and Pipeline
In today’s ultra-digital world, the information data provides empowers virtually everything. From how government, healthcare, and education functions to how businesses are run and managed and eCommerce products are sold, marketed, and advertised, there’s very little in today’s world that is not touched by data.
Data has also become the backbone of today’s innovative enterprise and business analytics. To back up the importance data plays in today’s ultra-competitive business environment, a 2019 article from Forbes cites a recent Deloitte survey that illustrates how data analytics helps companies make smarter decisions, better enables key strategic initiatives, and improves relationships with customers and business partners.
In short, it would not be an understatement to say that a business’s very survival depends on detailed and dynamic data management.
Too Much Data?
The only problem with data is there now might be too much of it. In fact, 2.5 quintillion bytes of data is being generated every day. Amazingly, only 0.5% of that data is analyzed! This infographic shows the staggering amount of data that was generated every minute of every day in 2020.
Along with the vast amounts of data currently out there, there are also countless processes to apply to it, as the data technology sector is continually providing new tools, protocols, and standards to help organizations better collect, analyze, and share information gleaned from data.
However, while they go a long way to streamlining data management processes, these technical advances have also greatly increased the complexity of data architectures. This complexity can handicap an organization’s ability to deliver new capabilities, maintain existing infrastructures, and safeguard the integrity of artificial intelligence models.
Corporations, in particular, are beginning to realize that crucial information and business processes need to be managed from an enterprise perspective rather than the traditional segmented approach.
Enter Data Pipeline Architecture
Here’s where data pipelines have increasingly come into play. Essentially, data pipelines carry raw data from different data sources and databases and sends it to data warehouses for analysis. Data pipeline architecture is layered, with each subsystem feeding into the next one until data reaches its destination.
Developers can build data pipelines by writing code and interfacing with software-as-a-service (SaaS) platforms manually. Nowadays, data analysts prefer using DPaaS (Data Pipeline as-a-service) where coding is not required.
Data Pipeline Architecture, Defined
Simply put, data pipeline architecture is the layout and structure of code and systems that copy, cleanse, or transform source data and then routes it to destinations like data warehouses and data lakes where raw data is assembled. In a process where speed is often of the essence, data pipeline speed is determined by three factors:
Rate, also known as throughput, is how much data a pipeline can process within a specified amount of time.
Latency is the time needed for a single unit of data to travel through the pipeline. Latency pertains more to response time than to throughput.
Reliability requires all of a data pipeline’s connected systems to be fault free. A reliable data pipeline with built-in auditing, logging, and validation helps improve and ensure data quality.
When designing its data pipeline, it’s important for an organization to consider business objectives, cost, and the types and availability of resources. As well, accurate and efficient data integration processes play an important role in corralling and analyzing data.
Building an Enterprise Data Architecture
Before developers can get to building their data architecture pipelines, three critical data factors must be first determined:
Knowing the nature of your pipeline’s input data will determine the format used to store data, what to do when data goes missing, and the technology to use in the rest of the pipeline.
When building an enterprise data architecture pipeline, the output data must be accessible and manipulable given an end-user’s possible lack of data engineering expertise. Analytics engines can help with easy integration between data ecosystems and analytics warehouses.
How much data can the pipeline absorb?
Hardware and software infrastructures need to be able to keep up with a sudden change in data volume. The long-term viability of the business can often be determined by the overall scalability of its data system pipeline. The goal is to not overload a data system as the organization scales and grows.
4 Basic Steps
In very simple terms, here are four steps towards building an enterprise-level data pipeline architecture:
1. Document core business processes
When designing and implementing their enterprise data architecture pipeline, organizations need to remember that the architecture must support its core business processes and not the other way around. So, the first step is to standardize and document core business processes across the entire enterprise.
Understanding the difference between core business processes and business solution processes is also an important part of this first step process.
2. Identify business objects
The next step is to identify all of the business objects that are used or supported by the core business processes. Simply defined, a business object is an application data container, such as an invoice or even a customer. Data is exchanged between components by business objects.
For each business object, data that should be associated with an object needs to be identified in order to differentiate it from other business objects. This is done by assigning each object with a unique identifier and a description of exactly what that business object represents, an example of the data, any desired special formats, and a statement of why it needs to be collected.
3. Confirm business data
Identifying data that needs to be collected in order to obtain a desired business outcome will give an organization a better picture of the effectiveness of a particular business process.
Identifying how data needs to be processed within the business process involves documenting the data (or groups of data) to be communicated between business processes and tasks.
4. Build the Architecture
This final step entails building relationship models within the architecture to ensure that new systems and major upgrades to existing systems’ design will support the data standards and processes.
Once complete, a data pipeline architecture that accurately reflects an organization’s core business processes can significantly increase an organization’s data integrity, help determine the overall effects of modifying or adding new business processes into the mix, and gives a detailed depiction of how data is represented across the entire enterprise.
This is, of course, a very simplified run-through of what can be a complicated endeavor, and each organization will require architecture characteristics that are unique to their individual core business processes. As with any major enterprise effort, designing a data pipeline architecture requires a lot of up-front planning, as well as devising a strategy with detailed implementation plans.
This video from Jonathan Tremblay, owner of Gretrix, LLC, and John Ament, Software and Data Engineering Manager at Trimble, Inc., goes deeper into the process of designing an enterprise-level data architecture and pipeline. It offers tips and tricks for establishing a robust data architecture, discusses how to handle high-bandwidth data integration, gives best practices on enhancing scalability and usability, and much more.